麻豆AV

Informal Systems Seminar (ISS), Centre for Intelligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Decisions (GERAD)

Speaker: Raihan Seraj

**听狈辞迟别听迟丑补迟听迟丑颈蝉听颈蝉听补听丑测产谤颈诲听别惫别苍迟.
** Note that this seminar does not take place at the usual time and location of ISS seminars.


惭别别迟颈苍驳听滨顿:听845听1388听1004听听听听听听听
笔补蝉蝉肠辞诲别:听痴滨厂厂

Abstract:

Human cognitive states, such as mental workload, play a pivotal role in decision making processes within human automation teams. Although subjective measures of mental workload can be obtained using standard questionnaires like the NASA-TLX, their administration is often impractical as it interferes with the primary tasks of the human operator. Therefore, it is of interest to estimate these subjective measures from less intrusive observations. Evidence suggests that mental workload is a dynamic process so incorporating historical measurements could reduce its estimation error. Additionally, the estimation of operator performance in human automation teams is essential in optimizing task effectiveness and facilitating efficient resource allocation. In this work, we present and compare different dynamic schemes to estimate an operator鈥檚 performance on classification tasks, i.e., classification accuracy and her subjective ratings on subscales of the NASA-TLX questionnaire, which measure mental workload across multiple dimensions. These schemes differ in the information available for estimation. We test these schemes on data collected from a scenario where a human and an automation perform a series of classification tasks for simulated mobile objects. Our analysis of the interaction data and the estimation schemes indicates that employing dynamic estimation for certain NASA-TLX subscale ratings leads to decreased estimation errors. However, similar conclusions cannot be drawn with certainty for the estimation of the operator classification accuracy.

Bio: 听

Raihan Seraj is a PhD candidate in the Department of Electrical and Computer Engineering, 麻豆AV.




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